From 576aaddfaf1dec031cdf580924c00f4b29b9b35a Mon Sep 17 00:00:00 2001 From: Xintao Date: Mon, 19 Sep 2022 01:08:15 +0800 Subject: [PATCH] support denoise strength for realesr-general-x4v3 --- inference_realesrgan.py | 53 ++++++++++++++++++++++++++++++++--------- realesrgan/utils.py | 33 +++++++++++++++++++++---- 2 files changed, 70 insertions(+), 16 deletions(-) diff --git a/inference_realesrgan.py b/inference_realesrgan.py index a24140e..ed25c55 100644 --- a/inference_realesrgan.py +++ b/inference_realesrgan.py @@ -3,6 +3,7 @@ import cv2 import glob import os from basicsr.archs.rrdbnet_arch import RRDBNet +from basicsr.utils.download_util import load_file_from_url from realesrgan import RealESRGANer from realesrgan.archs.srvgg_arch import SRVGGNetCompact @@ -19,10 +20,18 @@ def main(): type=str, default='RealESRGAN_x4plus', help=('Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus | ' - 'realesr-animevideov3 | realesr-general-x4v3 | realesr-general-wdn-x4v3')) + 'realesr-animevideov3 | realesr-general-x4v3')) parser.add_argument('-o', '--output', type=str, default='results', help='Output folder') - parser.add_argument('--model_path', type=str, default=None, help='Model path') + parser.add_argument( + '-dn', + '--denoise_strength', + type=float, + default=0.5, + help=('Denoise strength. 0 for weak denoise (keep noise), 1 for strong denoise ability. ' + 'Only used for the realesr-general-x4v3 model')) parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image') + parser.add_argument( + '--model_path', type=str, default=None, help='[Option] Model path. Usually, you do not need to specify it') parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored image') parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing') parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding') @@ -47,36 +56,58 @@ def main(): # determine models according to model names args.model_name = args.model_name.split('.')[0] - if args.model_name in ['RealESRGAN_x4plus', 'RealESRNet_x4plus']: # x4 RRDBNet model + if args.model_name == 'RealESRGAN_x4plus': # x4 RRDBNet model model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) netscale = 4 - elif args.model_name in ['RealESRGAN_x4plus_anime_6B']: # x4 RRDBNet model with 6 blocks + file_url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth' + elif args.model_name == 'RealESRNet_x4plus': # x4 RRDBNet model + model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) + netscale = 4 + file_url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth' + elif args.model_name == 'RealESRGAN_x4plus_anime_6B': # x4 RRDBNet model with 6 blocks model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4) netscale = 4 - elif args.model_name in ['RealESRGAN_x2plus']: # x2 RRDBNet model + file_url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth' + elif args.model_name == 'RealESRGAN_x2plus': # x2 RRDBNet model model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) netscale = 2 - elif args.model_name in ['realesr-animevideov3']: # x4 VGG-style model (XS size) + file_url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth' + elif args.model_name == 'realesr-animevideov3': # x4 VGG-style model (XS size) model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu') netscale = 4 - elif args.model_name in ['realesr-general-x4v3', 'realesr-general-wdn-x4v3']: # x4 VGG-style model (S size) + file_url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth' + elif args.model_name == 'realesr-general-x4v3': # x4 VGG-style model (S size) model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu') netscale = 4 + file_url = [ + 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth', + 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth' + ] # determine model paths if args.model_path is not None: model_path = args.model_path else: - model_path = os.path.join('experiments/pretrained_models', args.model_name + '.pth') + model_path = os.path.join('realesrgan/weights', args.model_name + '.pth') if not os.path.isfile(model_path): - model_path = os.path.join('realesrgan/weights', args.model_name + '.pth') - if not os.path.isfile(model_path): - raise ValueError(f'Model {args.model_name} does not exist.') + ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + for url in file_url: + # model_path will be updated + model_path = load_file_from_url( + url=url, model_dir=os.path.join(ROOT_DIR, 'realesrgan/weights'), progress=True, file_name=None) + + # use dni to control the denoise strength + dni_weight = None + if args.model_name == 'realesr-general-x4v3' and args.denoise_strength != 1: + wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3') + model_path = [model_path, wdn_model_path] + dni_weight = [args.denoise_strength, 1 - args.denoise_strength] # restorer upsampler = RealESRGANer( scale=netscale, model_path=model_path, + dni_weight=dni_weight, model=model, tile=args.tile, tile_pad=args.tile_pad, diff --git a/realesrgan/utils.py b/realesrgan/utils.py index 24d5d9d..e409360 100644 --- a/realesrgan/utils.py +++ b/realesrgan/utils.py @@ -29,6 +29,7 @@ class RealESRGANer(): def __init__(self, scale, model_path, + dni_weight=None, model=None, tile=0, tile_pad=10, @@ -49,22 +50,44 @@ class RealESRGANer(): f'cuda:{gpu_id}' if torch.cuda.is_available() else 'cpu') if device is None else device else: self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device - # if the model_path starts with https, it will first download models to the folder: realesrgan/weights - if model_path.startswith('https://'): - model_path = load_file_from_url( - url=model_path, model_dir=os.path.join(ROOT_DIR, 'realesrgan/weights'), progress=True, file_name=None) - loadnet = torch.load(model_path, map_location=torch.device('cpu')) + + if isinstance(model_path, list): + # dni + assert len(model_path) == len(dni_weight), 'model_path and dni_weight should have the save length.' + loadnet = self.dni(model_path[0], model_path[1], dni_weight) + else: + # if the model_path starts with https, it will first download models to the folder: realesrgan/weights + if model_path.startswith('https://'): + model_path = load_file_from_url( + url=model_path, + model_dir=os.path.join(ROOT_DIR, 'realesrgan/weights'), + progress=True, + file_name=None) + loadnet = torch.load(model_path, map_location=torch.device('cpu')) + # prefer to use params_ema if 'params_ema' in loadnet: keyname = 'params_ema' else: keyname = 'params' model.load_state_dict(loadnet[keyname], strict=True) + model.eval() self.model = model.to(self.device) if self.half: self.model = self.model.half() + def dni(self, net_a, net_b, dni_weight, key='params', loc='cpu'): + """Deep network interpolation. + + ``Paper: Deep Network Interpolation for Continuous Imagery Effect Transition`` + """ + net_a = torch.load(net_a, map_location=torch.device(loc)) + net_b = torch.load(net_b, map_location=torch.device(loc)) + for k, v_a in net_a[key].items(): + net_a[key][k] = dni_weight[0] * v_a + dni_weight[1] * net_b[key][k] + return net_a + def pre_process(self, img): """Pre-process, such as pre-pad and mod pad, so that the images can be divisible """